Similar to the nature of self-supervised training, fully supervised pre-training with additional image or video datasets has been widely explored in the literature. In [1]}, a frame-to-sequence method was proposed that used a pre-trained CNN as a feature extractor followed by an RNN to capture the temporal features. In [2]}, a multi-modal framework was proposed that used both image and video -level information with an ensemble of 2D and 3D CNNs. Here the 2D model acts as a pre-trained feature extractor which is followed by a NetVLAD layer [3]} which aggregates the temporal representation of videos. In [4]}, a generative approach was proposed to pre-train the network with a conditional generative adversarial network [5]}.
